VQE implementation with estimator primitive

qiskit/algorithms/__init__.py

@@ -183,10 +183,12 @@
    linear_solvers
 
 
-Minimum Eigensolvers
---------------------
+Minimum Eigen Solvers
+---------------------
 
 Algorithms that can find the minimum eigenvalue of an operator.
+These algorithms are pending depreciation. One should instead make use of the
+Minimum Eigensolver classes in the section below, which leverage Runtime primitives.
 
 .. autosummary::
    :toctree: ../stubs/
@@ -203,6 +205,16 @@
    QAOA
    VQE
 
+Minimum Eigensolvers
+--------------------
+
+Algorithms that can find the minimum eigenvalue of an operator and leverage primitives.
+
+.. autosummary::
+   :toctree: ../stubs/
+
+   minimum_eigensolvers
+
 
 Optimizers
 ----------

qiskit/algorithms/minimum_eigensolvers/__init__.py

@@ -0,0 +1,48 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+============================================================================
+Minimum Eigensolvers Package (:mod:`qiskit.algorithms.minimum_eigensolvers`)
+============================================================================
+
+.. currentmodule:: qiskit.algorithms.minimum_eigensolvers
+
+Minimum Eigensolvers
+====================
+.. autosummary::
+   :toctree: ../stubs/
+
+   MinimumEigensolver
+   NumPyMinimumEigensolver
+   VQE
+
+.. autosummary::
+   :toctree: ../stubs/
+
+   MinimumEigensolverResult
+   NumPyMinimumEigensolverResult
+   VQEResult
+"""
+
+from .minimum_eigensolver import MinimumEigensolver, MinimumEigensolverResult
+from .numpy_minimum_eigensolver import NumPyMinimumEigensolver, NumPyMinimumEigensolverResult
+from .vqe import VQE, VQEResult
+
+__all__ = [
+    "MinimumEigensolver",
+    "MinimumEigensolverResult",
+    "NumPyMinimumEigensolver",
+    "NumPyMinimumEigensolverResult",
+    "VQE",
+    "VQEResult",
+]

qiskit/algorithms/minimum_eigensolvers/minimum_eigensolver.py

@@ -0,0 +1,100 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""The minimum eigensolver interface and result."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+from qiskit.opflow import PauliSumOp
+from qiskit.quantum_info.operators.base_operator import BaseOperator
+
+from ..algorithm_result import AlgorithmResult
+from ..list_or_dict import ListOrDict
+
+
+class MinimumEigensolver(ABC):
+    """The minimum eigensolver interface.
+
+    Algorithms that can compute a minimum eigenvalue for an operator may implement this interface to
+    allow different algorithms to be used interchangeably.
+    """
+
+    @abstractmethod
+    def compute_minimum_eigenvalue(
+        self,
+        operator: BaseOperator | PauliSumOp,
+        aux_operators: ListOrDict[BaseOperator | PauliSumOp] | None = None,
+    ) -> "MinimumEigensolverResult":
+        """
+        Computes the minimum eigenvalue. The ``operator`` and ``aux_operators`` can be supplied here
+        and if not ``None`` will override any already set into algorithm so it can be reused with
+        different operators. While an ``operator`` is required by algorithms, ``aux_operators`` are
+        optional.
+
+        Args:
+            operator: Qubit operator of the observable.
+            aux_operators: Optional list of auxiliary operators to be evaluated with the
+                parameters of the minimum eigenvalue main result and their expectation values
+                returned. For instance in chemistry these can be dipole operators and total particle
+                count operators, so we can get values for these at the ground state.
+
+        Returns:
+            A minimum eigensolver result.
+        """
+        return MinimumEigensolverResult()
+
+    @classmethod
+    def supports_aux_operators(cls) -> bool:
+        """Whether computing the expectation value of auxiliary operators is supported.
+
+        If the minimum eigensolver computes an eigenvalue of the main ``operator`` then it can
+        compute the expectation value of the ``aux_operators`` for that state. Otherwise they will
+        be ignored.
+
+        Returns:
+            True if aux_operator expectations can be evaluated, False otherwise
+        """
+        return False
+
+
+class MinimumEigensolverResult(AlgorithmResult):
+    """Minimum eigensolver result."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._eigenvalue = None
+        self._aux_operator_eigenvalues = None
+
+    @property
+    def eigenvalue(self) -> complex | None:
+        """The computed minimum eigenvalue."""
+        return self._eigenvalue
+
+    @eigenvalue.setter
+    def eigenvalue(self, value: complex) -> None:
+        self._eigenvalue = value
+
+    @property
+    def aux_operator_eigenvalues(self) -> ListOrDict[tuple[complex, tuple[complex, int]]] | None:
+        """The aux operator expectation values.
+
+        These values are in fact tuples formatted as (mean, (variance, shots)).
+        """
+        return self._aux_operator_eigenvalues
+
+    @aux_operator_eigenvalues.setter
+    def aux_operator_eigenvalues(
+        self, value: ListOrDict[tuple[complex, tuple[complex, int]]]
+    ) -> None:
+        self._aux_operator_eigenvalues = value

qiskit/algorithms/minimum_eigensolvers/numpy_minimum_eigensolver.py

@@ -0,0 +1,105 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""The NumPy minimum eigensolver algorithm and result."""
+
+from __future__ import annotations
+
+from typing import Callable
+import logging
+import numpy as np
+
+from qiskit.opflow import PauliSumOp
+from qiskit.quantum_info.operators.base_operator import BaseOperator
+
+# TODO this path will need updating
+from ..eigen_solvers.numpy_eigen_solver import NumPyEigensolver
+from .minimum_eigensolver import MinimumEigensolver, MinimumEigensolverResult
+from ..list_or_dict import ListOrDict
+
+logger = logging.getLogger(__name__)
+
+
+class NumPyMinimumEigensolver(MinimumEigensolver):
+    """
+    The NumPy minimum eigensolver algorithm.
+    """
+
+    def __init__(
+        self,
+        filter_criterion: Callable[
+            [list | np.ndarray, float, ListOrDict[float] | None], bool
+        ] = None,
+    ) -> None:
+        """
+        Args:
+            filter_criterion: callable that allows to filter eigenvalues/eigenstates. The minimum
+                eigensolver is only searching over feasible states and returns an eigenstate that
+                has the smallest eigenvalue among feasible states. The callable has the signature
+                ``filter(eigenstate, eigenvalue, aux_values)`` and must return a boolean to indicate
+                whether to consider this value or not. If there is no feasible element, the result
+                can even be empty.
+        """
+        self._eigensolver = NumPyEigensolver(filter_criterion=filter_criterion)
+
+    @property
+    def filter_criterion(
+        self,
+    ) -> Callable[[list | np.ndarray, float, ListOrDict[float] | None], bool] | None:
+        """Returns the criterion for filtering eigenstates/eigenvalues."""
+        return self._eigensolver.filter_criterion
+
+    @filter_criterion.setter
+    def filter_criterion(
+        self,
+        filter_criterion: Callable[[list | np.ndarray, float, ListOrDict[float] | None], bool],
+    ) -> None:
+        self._eigensolver.filter_criterion = filter_criterion
+
+    @classmethod
+    def supports_aux_operators(cls) -> bool:
+        return NumPyEigensolver.supports_aux_operators()
+
+    def compute_minimum_eigenvalue(
+        self,
+        operator: BaseOperator | PauliSumOp,
+        aux_operators: ListOrDict[BaseOperator | PauliSumOp] | None = None,
+    ) -> NumPyMinimumEigensolverResult:
+        super().compute_minimum_eigenvalue(operator, aux_operators)
+        eigensolver_result = self._eigensolver.compute_eigenvalues(operator, aux_operators)
+        result = NumPyMinimumEigensolverResult()
+        if eigensolver_result.eigenvalues is not None and len(eigensolver_result.eigenvalues) > 0:
+            result.eigenvalue = eigensolver_result.eigenvalues[0]
+            result.eigenstate = eigensolver_result.eigenstates[0]
+            if eigensolver_result.aux_operator_eigenvalues:
+                result.aux_operator_eigenvalues = eigensolver_result.aux_operator_eigenvalues[0]
+
+        logger.debug("NumPy minimum eigensolver result: %s", result)
+
+        return result
+
+
+class NumPyMinimumEigensolverResult(MinimumEigensolverResult):
+    """NumPy minimum eigensolver result."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._eigenstate = None
+
+    @property
+    def eigenstate(self) -> np.ndarray | None:
+        """Returns the eigenstate corresponding to the computed minimum eigenvalue."""
+        return self._eigenstate
+
+    @eigenstate.setter
+    def eigenstate(self, value: np.ndarray) -> None:
+        self._eigenstate = value